In this paper, we consider the problem of local risk minimization on the basis of empirical data, which is a generalization of the problem of global risk minimization. A new local risk regularization scheme is proposed. The error estimate for the proposed algorithm is obtained by using probabilistic estimates for integral operators. Experiments are presented to illustrate the general theory. Simulation results on several artificial real datasets show that the local risk regularization algorithm has better performance. © 2010 World Scientific Publishing Company.